Department of Wildland Resources and the Ecology Center, Utah State University, 5230 Old Main Hill, Logan, Utah, 84322, USA.
James C. Kennedy Endowed Chair in Wetland and Waterfowl Conservation, Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, 80523, USA.
Ecol Appl. 2017 Oct;27(7):2102-2115. doi: 10.1002/eap.1594. Epub 2017 Sep 6.
Identifying the demographic parameters (e.g., reproduction, survival, dispersal) that most influence population dynamics can increase conservation effectiveness and enhance ecological understanding. Life table response experiments (LTRE) aim to decompose the effects of change in parameters on past demographic outcomes (e.g., population growth rates). But the vast majority of LTREs and other retrospective population analyses have focused on decomposing asymptotic population growth rates, which do not account for the dynamic interplay between population structure and vital rates that shape realized population growth rates (λt=Nt+1/Nt) in time-varying environments. We provide an empirical means to overcome these shortcomings by merging recently developed "transient life-table response experiments" with integrated population models (IPMs). IPMs allow for the estimation of latent population structure and other demographic parameters that are required for transient LTRE analysis, and Bayesian versions additionally allow for complete error propagation from the estimation of demographic parameters to derivations of realized population growth rates and perturbation analyses of growth rates. By integrating available monitoring data for Lesser Scaup over 60 yr, and conducting transient LTREs on IPM estimates, we found that the contribution of juvenile female survival to long-term variation in realized population growth rates was 1.6 and 3.7 times larger than that of adult female survival and fecundity, respectively. But a persistent long-term decline in fecundity explained 92% of the decline in abundance between 1983 and 2006. In contrast, an improvement in adult female survival drove the modest recovery in Lesser Scaup abundance since 2006, indicating that the most important demographic drivers of Lesser Scaup population dynamics are temporally dynamic. In addition to resolving uncertainty about Lesser Scaup population dynamics, the merger of IPMs with transient LTREs will strengthen our understanding of demography for many species as we aim to conserve biodiversity during an era of non-stationary global change.
确定对种群动态影响最大的人口统计学参数(例如繁殖、生存、扩散)可以提高保护效果并增强生态理解。生命表响应实验(LTRE)旨在分解参数变化对过去人口统计结果(例如种群增长率)的影响。但是,绝大多数 LTRE 和其他回顾性人口分析都集中在分解渐近种群增长率上,而这些分析并未考虑到在时变环境中塑造实际种群增长率(λt=Nt+1/Nt)的种群结构和重要率之间的动态相互作用。我们通过将最近开发的“暂态生命表响应实验”与综合种群模型(IPM)合并,提供了一种克服这些缺点的方法。IPM 允许估计潜在的种群结构和其他人口参数,这些参数是暂态 LTRE 分析所必需的,贝叶斯版本还允许从人口参数的估计到实际种群增长率的推导以及增长率的扰动分析,完全传播误差。通过整合 60 多年来对小绒鸭的可用监测数据,并对 IPM 估计值进行暂态 LTRE,我们发现,幼雌存活率对实际种群增长率长期变化的贡献分别比成年雌存活率和繁殖力大 1.6 倍和 3.7 倍。但是,繁殖力的持续长期下降解释了 1983 年至 2006 年间数量下降的 92%。相比之下,成年雌鸟存活率的提高推动了小绒鸭数量自 2006 年以来的适度恢复,这表明小绒鸭种群动态的最重要人口驱动因素是时间动态的。除了解决小绒鸭种群动态的不确定性外,将 IPM 与暂态 LTRE 合并将加强我们对许多物种的人口统计学的理解,因为我们旨在在非平稳全球变化的时代保护生物多样性。